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haiku.rag/docs/index.md
Yiorgis Gozadinos 9ee64a33e0
Remove A2A from haiku.rag, turn it into an example
2025-11-07 13:31:51 +02:00

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haiku.rag

haiku.rag is a Retrieval-Augmented Generation (RAG) library built to work with LanceDB as a local vector database. It uses LanceDB for storing embeddings and performs semantic (vector) search as well as full-text search combined through native hybrid search with Reciprocal Rank Fusion. Both open-source (Ollama, MixedBread AI) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.

Features

  • Local LanceDB: No need to run additional servers
  • Support for various embedding providers: Ollama, VoyageAI, OpenAI or add your own
  • Native Hybrid Search: Vector search combined with full-text search using native LanceDB RRF reranking
  • Reranking: Optional result reranking with MixedBread AI or Cohere
  • Question Answering: Built-in QA agents using Ollama, OpenAI, or Anthropic
  • File monitoring: Automatically index files when run as a server
  • Extended file format support: Parse 40+ file formats including PDF, DOCX, HTML, Markdown, code files and more. Or add a URL!
  • MCP server: Exposes functionality as MCP tools
  • CLI commands: Access all functionality from your terminal
    • Add sources from text, files, or URLs, optionally with a human‑readable title
  • Python client: Call haiku.rag from your own python applications

Quick Start

Install haiku.rag:

uv pip install haiku.rag

Use from Python:

from haiku.rag.client import HaikuRAG

async with HaikuRAG("database.lancedb") as client:
    # Add a document
    doc = await client.create_document("Your content here")

    # Search documents
    results = await client.search("query")

    # Ask questions
    answer = await client.ask("Who is the author of haiku.rag?")

Or use the CLI:

haiku-rag add "Your document content"
haiku-rag add "Your document content" --meta author=alice
haiku-rag add-src /path/to/document.pdf --title "Q3 Financial Report" --meta source=manual
haiku-rag search "query"
haiku-rag ask "Who is the author of haiku.rag?"

Documentation

  • Getting started - Tutorial
  • Installation - Install haiku.rag with different providers
  • Configuration - Environment variables and settings
  • CLI - Command line interface usage
  • Server - File monitoring and server mode
  • MCP - Model Context Protocol integration
  • Python - Python API reference
  • Agents - QA agent and multi-agent research

License

This project is licensed under the MIT License.